Local Datavores Workshop LGA – July 13

Sarah Henry – GM Connect and Head of Intelligence and Performance – Manchester City Council

s.henry@manchester.gov.uk A families database – from an individual to a holistic view

People, and especially children, are affected – positively, and sometimes negatively - by their family circumstances and the people around them.

But what we collectively know – the data - sits in systems about individuals so we need:

• To link individuals to ‘families’ and ‘households’ • Intelligent linkages between individuals e.g. family relationship, mention on benefit claims • Linkages to organisations such as schools • Linkages to and tracking of interventions, services and outcomes, recognising the engagement with the family is not static Our system -

- Replaces and complements manual processes to find out what others know about the family – MASH, Panels and conferences, desk-top work etc
- Visualises the family and the issues
- Creates data that can be analysed over time
- Brings together 18 datasets including offending, ASB, absence from school, low attendance at school, various benefits (including out of work), debt, social care, children missing education, NEET, poverty indicators, substance misuse, mental ill health etc.

1. Find the Case / Family
2. Direct Links to Case
3. Immediate Family
4. Extended Family
5. Exploring the Issues
6. Looking for Links
7. Expanding the Links
8. Finding the Connection Challenges

• Information Governance • Technical – selection of a system • Information Governance • Developing and agreeing rules and processes – data architecture • Information Governance Data Protection and Security

• Underpinned by data sharing agreements and a Privacy Impact Assessment • Consent, where needed and appropriate • Removal of the decision to share or not to share away from individual workers – consistency and corporate accountability • Who should access? – Restrictions, training and access control • Policies and procedures – including security, retention, destruction • Footprints Legal gateways

- Human Rights Act – right to life is absolute

- Right to privacy – conditional

- Duty to safeguard vulnerable people

- Local Government Act – general power of competence

- Duty to prevent crime (and fraud)

- Regulations designed specifically for programmes like Troubled Families

- And more… Critical Success factors

- Reasonable budget (but it didn’t break the bank!)

- Senior strategic sponsor

- Clarity of purpose – but agile enough to build in new things

- It wasn’t an IT project! Research and insight

• Understanding the impact on the system (so it can be planned)

• Understanding the causal factors behind the outcomes (so we provide the right service)

• Predictive modelling

• Ultimately leading to decision making tools Data Mining & Testing

There are numerous data mining techniques that could be used in an analysis of this large dataset.

Five techniques have been used in this project:

- Decision Tree
- Cluster Analysis
- Regression Analysis
- Sequence Analysis
- Spatial Analysis

The analysis has been based on 326,486 records, representing 21,584 individuals over a 5-year period.

Data included:

- Child in Need episodes
- Domestic Abuse incidents
- School Absence issues
- School Exclusions
- Anti-Social Behaviour incidents & legal actions
- Offences Decision Trees

Reviewing recent history and using the common decisions to predict a likely future decision

The most important attribute to predicting TF was the number of MiCARE CIN events associated with a family.

Troubled Families in general have far more MiCARE events associated with them – an average of 5.68, compared to 0.31 for the non-TF

School absence, number of ASBs and PRU also were deemed consistently important.

Number of adult offences was consistently the least important. Cluster Analysis

Cluster Analysis was performed on 2,028 families, where there had been a referral made to the FRS and where there was at least 2-years worth of event data.

Four clusters were identified. No completely distinct groups but there were still patterns in the data.

Cluster 1 – 689 families - No MiCARE CIN events but a quarter of all DA events in this cluster. Just under a third of families had absence, a quarter had exclusions, and 22% had offences but none involving minors.

Cluster 2 – 724 families - All had MiCARE CIN events. No school absence, but 13% had exclusions. 18% had offences, none involving minors.

Cluster 3 – 308 families - All had MiCARE CIN events. All had absence, 37% have exclusions. 17% had offences but none involving minors.

Cluster 4 – 307 families - Half had MiCARE CIN events. Half had absence, 62% have exclusions. All families had offences, 73% of these offences involving minors.

## Regression Analysis

### Variables in the Equation

| Step 1<sup>a</sup> | B | S.E. | Wald | df | Sig. | Exp(B) | direction | |---------------------|-------|-------|---------|----|-------|--------|-----------| | Previous CIN referral | 1.418 | .071 | 401.379 | 1 | .000 | 4.127 | + | | Age | -.054 | .009 | 33.884 | 1 | .000 | .948 | - | | Absence periods (0,1,2,3) | .227 | .076 | 8.959 | 1 | .003 | 1.255 | + | | Previous absence | -.096 | .033 | 8.245 | 1 | .004 | .909 | - | | Exclusions periods (0,1,2,3) | .084 | .093 | .814 | 1 | .367 | 1.087 | | | Previous exclusions | .012 | .035 | .110 | 1 | .740 | 1.012 | | | Person Offending (0,1,2,3) | -.133 | .185 | .516 | 1 | .473 | .876 | | | Previous Person Offending | .072 | .071 | 1.038 | 1 | .308 | 1.075 | | | Family Offending (0,1,2,3) | .292 | .064 | 20.586 | 1 | .000 | 1.339 | + | | Previous Family Offending | -.163 | .030 | 30.670 | 1 | .000 | .849 | - | | Constant | -3.454| .115 | 903.863 | 1 | .000 | .032 | |

<sup>a</sup> Variable(s) entered on step 1: Pre_CIN_R, Age, x_periods, total_abs, Exc, Exc_Pre, P_Off, P_Off_Pre, F_Off, F_Off_Per.

Provides an insight into a child's short-term life history

- Having a **previous Child in Need (CIN) referral** is a strong predictor of a new Child in Need referral
- An **increase in the frequency of Unauthorised Absence** (in previous 3 half-terms) is a predictor of CIN
- Exclusions and Personal offending are **not** significant
- An **increase in the frequency of Family offending** (in previous 3 half-terms) is a predictor of CIN
- The likelihood of a **CIN referral is less when the young person gets older** Sequence Analysis

**Figure 1** – this represents a subset of all family records, a purple colour indicates the presence of an event (e.g. receiving housing benefit, or having school absence events) whereas a turquoise colour indicates the absence of such events. c.80,000 records

**Figure 2** represents just the TF data (with the extra attribute of whether they have received any intervention treatment), this shows far more events occur for these families. c.3,000 records Spatial Analysis

Location of Troubled Families

These areas are characterized by similar median incomes, types of problems the TFUs have, and deprivation indices. However, they differ in terms of transience and predominant tenure.

Density of Troubled Families

TFUs are scattered across the city, with very few areas having no TFUs (Didsbury). The number of TFUs never exceeds 5% of all households in any LSOA. Substantial clusters are observed in Harpurhey, Gorton/Belle Vue, and Whythenshawe. Predictive Modelling

The techniques used in this project culminate to aid with Predictive Modelling, this would include:

- Using data mining to identify your clusters / cohorts
- Testing cluster / cohorts characteristics to identify the significant factors
- Apply the significant factors to the whole population to identify scale
- Use decision trees and sequence analysis type tools to test likely impact of decision for this broader cohort
- Use this to inform the business planning